Professor: Dr. Robert Brunner

Teaching Assistant: Edward Kim

Course Assistant: Samantha Thrush

This class is an asynchronous, online course. This course will introduce and explore advanced data science concepts through practical demonstration of algorithms and technologies on cloud computing systems.

Students will learn about the basic tasks in machine learning, including the importance of data preparation. Next, linear regression is introduced along with concepts like regularization and an extension to logistic regression. Supervised learning is introduced with examples for both classification and regression presented including naive Bayes, k-nn, SVM, decision trees, and ensemble techniques. Unsupervised techniques are presented with applications in both clustering and dimensional reduction. Specific application areas are explored for these machine learning techniques, including text analysis, network analysis, and social media analysis. The last part of the course focuses on cloud computing technologies, including Hadoop, MapReduce, NoSQL data stores, Spark, and streaming data analysis. The course concludes with a brief introduction of deep learning.

Students will be expected to use the course JupyterHub server, which requires a fairly modern web browser. While we do not recommend it, a modern tablet or smartphone can be used to access the course material. However, to write and run programs, it is much more efficient to use a standard computer with a regular keyboard. However, you can use publicly available computers (for example, in the Library) if necessary, to access the cloud resources in this class.

This class is open to sophomores, juniors, seniors and graduate students in any discipline who have met the required pre-requisites or have the permission of the instructor.

Please refer to the course syllabus for more information about course content and grading policies.

If you have any questions, or if something is not working properly, PLEASE look through the Moodle Q&A Forum and the course FAQ wiki page (please look at the right tool bar on the Github course page and click the icon labeled "Wiki" that looks like an open book), before either posting a new forum post or emailing the TA or the course instructor.

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